Abstract
This paper proposes a method for saving the optimized calculating time and maximizing the energy storage density of the superconducting magnet coil. The size of the coil is taken as the optimal objective. The genetic algorithm (GA) and the traditional particle swarm optimization (PSO) are analyzed to compare with the proposed PSO. Simulation results show that the improved PSO has faster convergence rate than the GA and stronger astringency than the traditional PSO. The proposed method is used to optimize the size of the superconducting energy storage coil on the premise that the total volume remains unchanged. Experimental results verify the effectiveness of the optimization method.
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